油气地质与采收率2026，Vol.33Issue(3)：169-179,11.DOI:10.13673/j.pgre.202412026
基于图神经网络的注采井网产油量预测方法
Oil production forecasting method for injection and production well patterns based on graph neural network
摘要
Abstract
Traditional neural network-based oil production forecasting methods fail to account for the mutual influence between injection and production wells.To address this limitation,this study proposed a graph neural network(GNN)-based oil production forecasting method for injection and production well patterns.The method integrates dilated convolutional neural networks(dilated CNNs)with causal convolutional neural networks(causal CNNs)to extract production dynamic features.An adaptive inter-well connectivity matrix was constructed based on reservoir geological characteristics,and a graph diffusion neural network(GDNN)was employed to capture inter-well connectivity information,thereby enhancing the forecasting capability of the GNN under injection and production well pattern conditions.By using production data from a well group comprising nine production wells and five injection wells in a block of the Daqing Oilfield as a case study,the hyperparameters of the GNN-based model were optimized,and the accuracy of the prediction results was validated.The results indicate that compared with traditional neural network models,the proposed GNN model significantly reduces prediction errors,with the mean absolute percentage error(MAPE)decreasing by 32.431%and the normalized deviation(ND)decreasing by 7.785%.The model generates an optimized inter-well connectivity matrix based on historical production data,ensuring the matrix aligns better with actual reservoir geological properties.This optimized matrix can subsequently assist in future oil production forecasting.Furthermore,optimizing hyperparameters,such as the maximum diffusion order,effectively improves prediction accuracy,confirming the necessity of hyperparameter optimization for GNN models.Verified with field production data,the proposed method demonstrates high prediction accuracy and strong robustness,indicating its applicability for oil production forecasting in actual oilfields under water injection development.关键词
图神经网络/多井产油量预测/注采数据分析/井间连通性分析/注水开发Key words
graph neural network/multi-well oil production prediction/injection and production data analysis/inter-well connectivity analysis/water injection development分类
能源科技引用本文复制引用
薛亮,魏瑞,聂捷,陈海洋,韩江峡,杨志成,廖勤拙..基于图神经网络的注采井网产油量预测方法[J].油气地质与采收率,2026,33(3):169-179,11.基金项目
国家自然科学基金面上项目"页岩气跨尺度多区复合运移机理与数据联合驱动的产能预测"(52274048),国家科技重大专项"页岩剩余气分布规律与储量动用评价技术研究"(2025ZD1405202),北京市首都高端领军人才聚集培养工程项目"智慧气藏数字孪生与全生命周期智能优化调控"(202504841068). （52274048）
